DockCoV2: a drug database against SARS-CoV-2
The current state of the COVID-19 pandemic is a global health crisis. From December 2019 to September 2020, SARS-CoV-2 has infected over 32 million people, and caused more than one million deaths worldwide. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus entry or replication. The Genomics team at Taiwan AI Labs collaborated with Professor Hsuan-Cheng Huang at National Yang-Ming University, Professor Chien-Yu Chen and Distinguished Professor Hsueh-Fen Juan at National Taiwan University under MOST and NTU supports to develop the database DockCoV2 (https://covirus.cc/drugs/) which aims to find effective antiviral drugs against SARS-CoV2.
The research team explores new opportunities for drug repurposing which is the process of finding new uses for existing approved drugs, and is believed to offer great benefits over de novo drug discovery, as well as to enable rapid clinical trials and regulatory review for COVID-19 therapy.
Here we develop the database, DockCoV2, by performing molecular docking analyses of seven proteins including spike, 3CLpro, PLpro, RdRp, N protein, ACE2, and TMPRSS2 with 2,285 FDA-approved and 1,478 NHI drugs. DockCoV2 also provides appropriate validation information with literature support. Several databases focus on delivering repurposing drugs against SARS-CoV-2. To our knowledge, no database provides a more up-to-date and comprehensive resource with drug-target docking results for repurposed drugs against SARS-CoV-2.
DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. DockCoV2 offers not only the related information of Docking structure and Ligand information but also Experimental data including biological assays, pathway information, and gene set enrichment analysis recruited from other validated databases. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. We also have released our scripts and source on github (https://github.com/ailabstw/DockCoV2).
Article link:
Ting-Fu Chen, Yu-Chuan Chang, Yi Hsiao, Ko-Han Lee, Yu-Chun Hsiao, Yu-Hsiang Lin, Yi-Chin Ethan Tu, Hsuan-Cheng Huang, Chien-Yu Chen, Hsueh-Fen Juan. DockCoV2: a drug database against SARS-CoV-2. Nucleic Acids Research (2020) https://doi.org/10.1093/nar/gkaa861.
Figure. The overview of the database content. In addition to the docking scores, DockCoV2 designed a joint panel section to provide the following related information: Docking structure, Ligand information, and Experimental data.
